---
title: "Donor Influence, Polarization, and Representation in an Era of Nationalized Politics^[Verification Materials: The data and materials required to verify the computational reproducibility of the results, procedures and analyses in this article are available on the Harvard Dataverse Network at www.google.com.]"
author: "David Beavers, Jeremiah Cha, Andrew O'Donohue"
date: "`r format(Sys.time(), '%d %B %Y')`"
output: pdf_document
indent: true
header-includes:
- \usepackage{setspace}
- \doublespacing
---

```{r setup, include=FALSE}
# Set working directory to wherever the replication dataset is stored
setwd("~/David/Harvard/2020_Fall/Gov2001/Replication_project/Final Submission Materials")

# Load packages
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(haven)
library(plm)
library(stargazer)
library(sjPlot)
library(ggplot2)
library(mvtnorm)
library(gridExtra)
library(broom)
theme_set(theme_sjplot())

options(dplyr.summarise.inform = FALSE)
set.seed(02138)

# Load all replication data for Kujala (2020)
candidate0210_weighted <- read_dta("candidate0210_data_weighted.dta")
candidate0210_data <- read_dta("candidate0210_data.dta")

# Load dataset we created that includes the *amount* given by each donor
# This data covers all donations to presidential candidates, 2002-10
amount <- read_dta("amount.dta")

# Load dataset that includes amount given to House candidates, 2002-10
amount_house <- read_dta("amount_house.dta")

# Load DIME candidate data
dime_all <- read_dta("dime0210_cand.dta") %>%
  rename(
    cand_dist = district
  ) %>%
  filter()
```

The classical theory of representation is predicated on the idea that constituents hold elected officials accountable for their legislative behavior at the ballot box. While this relationship is often taken for granted in normative discussions about American politics, the degree to which this theory is supported by quantitative evidence is one of the longest-standing questions in political science.

On the one hand, the school of thought advanced by Miller and Stokes (1963) holds that voters are too ill-informed to effectively hold their representatives accountable. Voters are by and large uninterested in party nominations and primary contests, ceding influence in this realm to interest groups, party activists, and other intense "policy demanders" (Bawn et al, 2012). Recent work has also shown that the nationalization of congressional races has diminished the electoral penalty for incumbents who diverge from their district preferences, as political parties and voters alike devote more attention to partisan control of national institutions (Bonica and Cox, 2018). Likewise, proponents of the social identity theory of partisanship posit that even moderate voters are unable to rein in ideologically extreme politicians due to the weak alignment between partisans’ policy preferences and partisan identification (Achen and Bartels, 2016).

On the other hand, a body of research has shown that incumbents who take extreme positions face electoral consequences (Canes-Wrone, Brady, and Cogan, 2002; Ansolabehere, Snyder, and Stewart, 2001). Ansolabehere, Snyder, and Stewart (2001) find small but consistent benefits to candidates for taking moderate positions among a subset of congressional candidates between 1874 and 1996. Canes-Wrone, Brady, and Cogan (2002) find that voters punish incumbents for ideologically extreme roll-call voting, relative to their co-partisans in Congress. Recent research examining individuals’ perceived issue agreement with their representatives suggests that Miller and Stokes’ (1963) findings may be overly pessimistic about the ability of voters to hold their representatives accountable (Ansolabehere and Kuriwaki, forthcoming).

One shortcoming of the extant research stems from the empirical difficulty of measuring the ideology of candidates and their primary, general election, and donor constituencies on the same ideological scale. Without common measures for ideology, previous research is unable to conduct a true Downsian test of whether legislators are punished for divergence from their district specifically or for their extremity in general (Downs, 1957).

Kujala (2020) creatively finds a solution to this research challenge. By rescaling donor and candidate ideology scores (CFscores) from the Database on Ideology, Money in Politics, and Elections (Bonica, 2016) to match district-level measures of general and primary constituency ideologies from the Cooperative Congressional Election Study (CCES), Kujala (2020) is able to assess candidate, donor, primary, and general electorate ideologies on a uniform scale. Using this measure, Kujala (2020) finds that donors exert significant influence in primary elections and contribute to the nomination of ideologically extreme congressional candidates. 

In this paper, we extend Kujala's (2020) findings by using his proposed methodology for rescaling candidate, donor, primary, and general electorate constituency ideologies to examine three unanswered questions about the influence of partisan donors.

First, do donors of different means exert different levels of influence on candidates? In Kujala (2020), a donor is a donor is a donor, whereas in reality, the volume of one’s political voice is a function of one’s wealth. If candidate access is the implicit mechanism behind Kujala (2020)’s theory, we would expect a donor who cuts a \$2,000 check to have more access and thus influence on candidate ideology than would a donor who gives \$20. Fortunately, we have access to donation size data, and thus are able to empirically test this hypothesis.

Second, what role do out-of-district donors play in shaping candidates’ ideologies? Congressional fundraising is increasingly dominated by out-of-district donors (Gimpel, Lee, and Pearson-Merkowitz, 2008; Hopkins, 2018) who disproportionately live in a handful of wealthy, densely populated districts anchored in major metropolitan areas (Gimpel, Lee, and Kamiski, 2006). For instance, Gimpel, Lee, and Pearson-Merkowitz (2008) show that candidates from the typical congressional district between 1996 and 2004 received approximately two-thirds of their itemized donations from out of district, and there is reason to believe this trend has continued since then (Hopkins, 2018). The decoupling of campaign funding from geographically bound districts presents an opportunity to empirically examine the influence of different donor constituencies and has important normative implications for how we think about representation in an era of nationalized politics.

Third, under what conditions is donor influence moderated? Different congressional candidates fill their campaign coffers from different sources, and we would expect differences in the degree to which individual donors influence congressional candidates’ ideological positioning based on the extent to which a given candidate’s fundraising strategy revolves around individual donors. Well-connected incumbents may be able to muster corporate political action committee donations that relegate individual donors’ contributions to a rounding error on their Federal Election Commission filing. We would expect the influence of individual donors to take a back seat to primary electorate influence among these candidates.

To address these three questions, we create several new independent variables not examined in Kujala's (2020) analysis, drawing on data from Bonica's (2016)  Database on Ideology, Money in Politics, and Elections (DIME). In particular, we use Bonica's (2016) data on the amount of a given donation, the geolocation of the donor, and the total dollar amount of receipts from individuals, political action committees, and candidates' personal finances to examine the questions highlighted above. 

Our paper treats these three questions in sequence, probing the microfoundations of how donors shape polarization and representation in House elections. Section 1 examines whether House candidates are more responsive to their large or small donor constituencies and identifies a significant difference in the responsiveness of Republican versus Democratic candidates. Section 2 distinguishes between in-state and out-of-state donors and analyzes whether candidates become less responsive to the donors in their district as they draw more donations from out of state. Section 3 explores yet another dilemma of representation by examining whether incumbent candidates become less responsive to their individual donors as they derive more of their funding from political action committees. Finally, Section 4 concludes by synthesizing our findings and outlining directions for further research in the period after the 2010 Supreme Court ruling on campaign finance in *Citizens United*.    

\vspace{20pt}

# 1. The Relationship Between Donor Size and Influence

In the 2002 election cycle, the first year of data that we analyze, the limit any donor could give to a congressional candidate was \$1,000. On March 27, 2002, President George W. Bush signed into law the Bipartisan Campaign Reform Act, which doubled the limit to \$2,000 effective for congressional elections beginning in 2004 and indexed it to inflation, such that by 2010, the final year of election and contribution data we consider, that limit was $2,400 per candidate per election. While Kujala (2020) shows that partisan campaign donor constituencies are on average more extreme than their partisan incumbent, open-seat, and primary constituency (see Kujala's Figure 1), he does not examine the relationship between donor size and ideology. 

As the quantity of money spent on U.S. elections has only grown, so too has scholarly research examining the relative influence of large versus small donors in driving polarization. For instance, Page et al. (2019, 25) identify the ultra-rich as a culprit, underscoring that billionaire donors “have engaged in extensive political actions that aim to move public policy in directions that most Americans oppose.” In contrast, other scholars find that while small donors are often idealized as a force that makes politics more responsive to the average citizen, they in fact tend to fuel more polarizing candidates (Pildes, 2019).

To examine these hypotheses, we construct donor ideology scores disaggregated by "large" and "small" donors. Using data on all contributions given by individuals to Democratic or Republican presidential candidates between 2002 and 2010\footnote{As described in Kujala (2020), it is necessary to construct ideology scores based on donations to \textit{presidential} rather than \textit{House} candidates to avoid endogeneity due to the way ideology scores are constructed in the DIME data. Specifically, Bonica (2016) locates donors' and recipients' ideologies simultaneously, based on which donors give to which recipients. Thus, if we were to consider House donations in constructing donor ideology, any statistical relationship subsequently found in our analyses is more likely to be due to how the measure was constructed than actually due to donor influence.}, we first group contributions by congressional district and by political party. We then classify these contributions as coming from "large" or "small" donors depending on whether the amount contributed exceeded the median contribution by donors of the same party in the same district during the period 2002-10. This definition accommodates the fact that the threshold for being a "large" donor varies significantly by district and party. The average Democratic donation to a House candidate during this period is \$486, compared to \$588 for Republicans. However, to ensure our results were robust to this modeling choice, we constructed and reran analyses for six additional operationalizations of the threshold for being a large donor.\footnote{Those operationalizations were: (1) defining "large" donors as anyone who gave \$1,000 or more; (2) defining "large" donors as anyone who gave over the national party median; and (3-6) defining "large" donors as anyone giving at or above the 60th, 70th, 80th and 90th percentiles, respectively, per party per district.} Our results were robust in each operationalization. 

The density plot below (**Figure 1**) reveals an interesting descriptive finding: Among both Democratic and Republican donors, large donors are generally *less* ideologically extreme than small donors. On the ideological scale used here, positive values of greater magnitude represent more conservative scores, while negative values of greater magnitude represent more liberal scores. We find that small donors are more narrowly distributed around a more extreme mean, whereas large donors are more broadly distributed around a less extreme mean. If large campaign donors are more moderate than small donors, and large donors have greater influence on candidates' ideology on account of their larger contribution, then we might expect donors to have less of a polarizing effect than Kujala (2020) suggests.

```{r figure 1, echo=FALSE, warning=FALSE, message=FALSE}
# Create a density plot of the distribution of donor ideology, by party 

# Drop all negative contributions; drop donations from committees; keep only 
# contributions to presidential candidates
data <- amount %>%
  filter(amount >= 0) %>%
  filter(contributortype == "I") %>%
  filter(seat == "federal:president")

# Operationalizing large vs. small donors

# Large donor defined as > median district donor by party

# Calculate median Democratic donor in each district
district_donor_avg_d <- data %>%
  group_by(contributordistrict00s) %>%
  filter(recipientparty == "100") %>%
  summarise(median_amount_d = median(amount))

# Calculate median Republican donor in each district
district_donor_avg_r <- data %>%
  group_by(contributordistrict00s) %>%
  filter(recipientparty == "200") %>%
  summarise(median_amount_r = median(amount))

# Calculate Democratic donor ideology
district_donor_median_d <- left_join(data, district_donor_avg_d, by = "contributordistrict00s") %>%
  filter(recipientparty == "100") %>%
  mutate(small_don_d = ifelse(amount < median_amount_d, "donor1_d_1", "donor1_d_0"),
         donor_d = contributorcfscore) %>%
  group_by(contributordistrict00s, small_don_d) %>%
  summarise(donor_d = mean(donor_d, na.rm = TRUE)) %>%
  pivot_wider(names_from = small_don_d, values_from = donor_d) %>% 
  rename(district = contributordistrict00s)

# Calculate Republican donor ideology
district_donor_median_r <- left_join(data, district_donor_avg_r, by = "contributordistrict00s") %>%
  filter(recipientparty == "200") %>%
  mutate(small_don_r = ifelse(amount < median_amount_r, "donor1_r_1", "donor1_r_0"),
         donor_r = contributorcfscore) %>%
  group_by(contributordistrict00s, small_don_r) %>%
  summarise(donor_r = mean(donor_r, na.rm = TRUE)) %>%
  pivot_wider(names_from = small_don_r, values_from = donor_r) %>% 
  rename(district = contributordistrict00s)

district_donor <- left_join(district_donor_median_d, district_donor_median_r, by = "district")

dat <- data.frame(dens = c(district_donor$donor1_d_0, district_donor$donor1_d_1,
                           district_donor$donor1_r_0, district_donor$donor1_r_1),
                  Legend = rep(c("Dem Big","Dem Small","Rep Big","Rep Small"), 
                              each = nrow(district_donor)))

ggplot(dat, aes(x = dens, fill = Legend)) + geom_density(alpha = 0.5) + 
  ggtitle("Figure 1: Ideological Distribution of Large vs. Small Donors by Party") + 
  xlab("Ideological Positioning of Individual Donors") + 
  ylab("Density")
```

   
   
Although **Figure 1** shows that large donors of both parties are less extreme than small donors, it remains to be seen how both sets of donors compare to their districts' primary and general electorate ideologies and to winning candidates' ideologies. To compare the ideological extremity of donor constituencies, primary electorates, and general electorates across different districts, we rescaled the ideological measure above using Kujala's (2020) methodology. **Figure 2** demonstrates that the median small donor and median large donor constituency are both more ideologically extreme than the median major party primary winner in House elections. On this scale, higher values of absolute extremity indicate more liberal Democrats and more conservative Republicans in absolute terms. 

Taken together, these two sets of descriptive statistics underscore the basic puzzles: Do donors of different means differently influence candidates' ideological positioning, and, if so, is it more ideologically extreme small donors or more well-resourced large donors who are pulling House candidates toward their ideological positioning?

\vspace{14pt}

```{r figure 2, echo=FALSE, warning=FALSE, message=FALSE}
# Create a data frame with the distribution of small donor, large donor, primary
# electorate, and general electorate ideology (absolute extremity)
dat2 <- data.frame(dens = c(candidate0210_data$abext_id7_ge, # general electorate extremity 
                    candidate0210_data$abext_id7_p, # primary electorate extremity 
                    candidate0210_data$abext_cf7_don_1, # small donor extremity 
                    candidate0210_data$abext_cf7_don_0), # large donor extremity 
                  Legend = rep(c("General Electorate","Primary Electorate",
                                "Small Donors","Large Donors"), 
                              each = nrow(candidate0210_data)))

# Create a subset of primary winners
primary_winners <- candidate0210_data %>% 
  filter(., win_p == 1)

# Create a density plot
ggplot(dat2, aes(x = dens, fill = Legend)) + geom_density(alpha = 0.5) + 
  ggtitle("Figure 2: Ideological Extremity of District Constituencies") + 
  xlab("Absolute Ideological Extremity") + 
  ylab("Density") + 
  geom_vline(xintercept = median(primary_winners$abext_cf7)) + 
  geom_text(x = median(primary_winners$abext_cf7) - .9, y = 3, 
            label="Ideology of \n Median Primary Winner")
```

To answer these questions, we regress small and large donor ideological distance from district on nominee ideological distance from district. Contrary to scholarly arguments highlighting that one category of donors is more polarizing that the other, our results in **Table 1** demonstrate that *both* large and small donors are associated with more ideologically extreme primary election winners. In **Table 1**, the dependent variable is a nominee’s ideological distance from her district, where the district's ideology is defined using CCES survey data on the ideology of district residents. Across all the regressions below, we find that as small and large donor constituencies become more extreme relative to a candidate's district, the candidate herself tends to become more extreme. This relationship holds for primary winners who are incumbents in the House, challengers who unseat an incumbent in a House election, and nominees in an "open-seat" race (i.e., one without an incumbent). For each category of nominee, the coefficients for large versus small donors are not distinguishable at the 95\% confidence level, indicating that candidates are not disproportionately responsive to one class of donors. 

```{r table 1, echo=FALSE, results = 'asis'}
# Table 1 (Distance from the district)

## Incumbent

### Only Small Donors
all_inc_small <- candidate0210_data %>% 
  filter(., type_p == 1 & win_p == 1) %>%
  rename(Incumbent = distge_cf7) %>% 
  plm(Incumbent ~ distge_cf7_don_1 # Small donor constituency dist. from district
      + distge_id7_p # Primary constituency distance from district
      + democrat, # Democrat
      data = ., 
      index = "year", model = "within")

### Only Large Donors
all_inc_large <- candidate0210_data %>% 
  filter(., type_p == 1 & win_p == 1) %>%
  rename(Incumbent = distge_cf7) %>% 
  plm(Incumbent ~ distge_cf7_don_0 # Large donor constituency dist. from district
      + distge_id7_p # Primary constituency distance from district
      + democrat, # Democrat
      data = ., 
      index = "year", model = "within")

## Challengers

### Only Small Donors
all_chal_small <- candidate0210_data %>% 
  filter(., type_p == 2 & win_p == 1) %>% 
  rename(Challenger = distge_cf7) %>% 
  plm(Challenger ~ distge_cf7_don_1 # Small donor constituency dist. from district
      + distge_id7_p + democrat, data = ., 
      index = "year", method = "within")

### Only Large Donors
all_chal_large <- candidate0210_data %>% 
  filter(., type_p == 2 & win_p == 1) %>% 
  rename(Challenger = distge_cf7) %>% 
  plm(Challenger ~ distge_cf7_don_0 # Large donor constituency dist. from district
      + distge_id7_p + democrat, data = ., 
      index = "year", method = "within")

## Open-Seat Nominees

### Only Small Donors
all_open_small <- candidate0210_data %>% 
  filter(., type_p == 3 & win_p == 1) %>% 
  rename(OpenSeat = distge_cf7) %>% 
  plm(OpenSeat ~ distge_cf7_don_1  # Small donor constituency dist. from district
      + distge_id7_p + democrat, data = ., 
      index = "year", method = "within")

### Only Large Donors
all_open_large <- candidate0210_data %>% 
  filter(., type_p == 3 & win_p == 1) %>% 
  rename(OpenSeat = distge_cf7) %>% 
  plm(OpenSeat ~ distge_cf7_don_0  # Large donor constituency dist. from district
      + distge_id7_p + democrat, data = ., 
      index = "year", method = "within")


stargazer(all_inc_small, all_inc_large, # Incumbent regressions
          all_chal_small, all_chal_large, # Challenger regressions
          all_open_small, all_open_large, # Open-seat regression
          title = "The Effect of Partisan Constituencies on the Divergence of Nominees from Their District, 2002–10",
          dep.var.labels = c("Incumbent", "Challenger", "Open-Seat"),
          dep.var.caption = "",
          column.labels = rep(c("Small Only", "Large Only"),3),
          covariate.labels = c("Small Donor \\\\ Constituency \\\\ Distance \\\\ from District",
                               "Large Donor \\\\ Constituency \\\\ Distance \\\\ from District",
                               "Primary \\\\ Constituency \\\\ Distance \\\\ from District",
                               "Democrat"),
          column.sep.width = "-5pt",
          omit.stat = c("f", "adj.rsq"),
          digits = 2,
          header = FALSE)
```

```{r coefficient test table 1, include=FALSE, message=FALSE, warning=FALSE}
# Test to see if the coefficients in regressions 1 and 2 are distinguishable
ci_all_inc_small <- confint_tidy(all_inc_small, conf.level = 0.95)
ci_all_inc_large <- confint_tidy(all_inc_large, conf.level = 0.95)

# Observe that the coefficients on small donors vs. large donors are not distinguishable 
# for incumbent House races
ci_all_inc_small[1,]
ci_all_inc_large[1,]
```
   
\newpage 

While **Table 1** finds that candidates are responsive to both their large and small donors, **Table 2** demonstrates that there are significant differences by political party. To compare whether nominees from different parties are more or less responsive to their donor constituencies, we created a measure of donor ideology that weights large donors’ ideology scores more than small donors’ scores. To construct this weighted measure, we multiply each individual donor's ideology score by the amount of their donation divided by the total receipts from their district. We find that this weighted measure is positive and statistically significant at the 95\% confidence level across all three types of House candidates and across the two parties. 

However, **Table 2** also demonstrates that among incumbent or challenger House candidates, the coefficient for weighted donor ideology is greater for Republicans—a finding distinguishable at the 95\% confidence level. (Among House candidates in open-seat races, this difference in coefficients between Democratic and Republican nominees is distinguishable at the 90\% confidence level.) The coefficients on primary constituency distance from district are also illuminating. Among Democrats, this effect remains positive and statistically significant across all candidate types. Among Republicans, primary constituency distance from district does not have a significant positive association with nominee divergence from the district for any of the candidate types.

```{r table 2, echo=FALSE, results = 'asis'}
# Table 3 (Distance from the district)

## Incumbent

### All
all_inc <- candidate0210_weighted %>% 
  filter(., type_p == 1 & win_p == 1) %>%
  rename(Incumbent = distge_cf7) %>% 
  plm(Incumbent ~ distge_cf7_don # Weighted donor constituency dist. from district
      + distge_id7_p # Primary constituency distance from district
      + democrat, # Democrat
      data = ., 
      index = "year", model = "within")

### Dems
dem_inc <- candidate0210_weighted %>% 
  filter(., type_p == 1 & win_p == 1 & democrat == 1) %>%
  rename(Incumbent = distge_cf7) %>% 
  plm(Incumbent ~ distge_cf7_don # Weighted donor constituency dist. from district
      + distge_id7_p, # Primary constituency distance from district
      data = ., 
      index = "year", model = "within")

### Reps
reps_inc <- candidate0210_weighted %>% 
  filter(., type_p == 1 & win_p == 1 & democrat == 0) %>%
  rename(Incumbent = distge_cf7) %>% 
  plm(Incumbent ~ distge_cf7_don # Weighted donor constituency dist. from district
      + distge_id7_p, # Primary constituency distance from district
      data = ., 
      index = "year", model = "within")

## Challengers

### All
all_chal <- candidate0210_weighted %>% 
  filter(., type_p == 2 & win_p == 1) %>% 
  rename(Challenger = distge_cf7) %>% 
  plm(Challenger ~ distge_cf7_don
      + distge_id7_p + democrat, data = ., 
      index = "year", method = "within")

### Dems
dem_chal <- candidate0210_weighted %>% 
  filter(., type_p == 2 & win_p == 1 & democrat == 1) %>% 
  rename(Challenger = distge_cf7) %>% 
  plm(Challenger ~ distge_cf7_don
      + distge_id7_p, data = ., 
      index = "year", method = "within")

### Reps
reps_chal <- candidate0210_weighted %>% 
  filter(., type_p == 2 & win_p == 1 & democrat == 0) %>% 
  rename(Challenger = distge_cf7) %>% 
  plm(Challenger ~ distge_cf7_don
      + distge_id7_p, data = ., 
      index = "year", method = "within")

## Open-Seat Nominees

### All
all_open <- candidate0210_weighted %>% 
  filter(., type_p == 3 & win_p == 1) %>% 
  rename(OpenSeat = distge_cf7) %>% 
  plm(OpenSeat ~ distge_cf7_don
      + distge_id7_p + democrat, data = ., 
      index = "year", method = "within")

### Dems
dem_open <- candidate0210_weighted %>% 
  filter(., type_p == 3 & win_p == 1 & democrat == 1) %>% 
  rename(OpenSeat = distge_cf7) %>% 
  plm(OpenSeat ~ distge_cf7_don
      + distge_id7_p, data = ., 
      index = "year", method = "within")

### Reps
reps_open <- candidate0210_weighted %>% 
  filter(., type_p == 3 & win_p == 1 & democrat == 0) %>% 
  rename(OpenSeat = distge_cf7) %>% 
  plm(OpenSeat ~ distge_cf7_don
      + distge_id7_p, data = ., 
      index = "year", method = "within")

stargazer(all_inc, dem_inc, reps_inc, # Incumbent regressions
          all_chal, dem_chal, reps_chal, # Challenger regressions
          all_open, dem_open, reps_open, # Open-seat regressions
          title = "The Effect of Partisan Constituencies on the Divergence of Nominees from Their District, 2002–10",
          dep.var.labels = c("Incumbent", "Challenger", "Open-Seat"),
          dep.var.caption = "",
          column.labels = rep(c("All", "Dems", "Reps"),3),
          covariate.labels = c("Weighted Donor \\\\ Constituency \\\\ Distance \\\\ from District",
                               "Primary \\\\ Constituency \\\\ Distance \\\\ from District",
                               "Democrat"),
          column.sep.width = "-5pt",
          omit.stat = c("f", "adj.rsq"),
          digits = 2,
          header = FALSE)
```

```{r coefficient test table 2, include=FALSE, message=FALSE, warning=FALSE}
# Test to see if the coefficients in regressions 8 and 9 are distinguishable
ci_dem_open <- confint_tidy(dem_open, conf.level = 0.90)
ci_reps_open <- confint_tidy(reps_open, conf.level = 0.90)

# Observe that the coefficients on small donors vs. large donors are not distinguishable 
# for incumbent House races
ci_dem_open[1,]
ci_reps_open[1,]
```

\newpage

Taken together, these results suggest heterogeneous effects by party on the influence of donor constituency ideology: While Democrats and Republicans alike appear to be responsive to their donors' ideologies, Republicans appear more likely to do so at the expense of responsiveness to their primary constituency. The construction of the weighted ideology score allows us to cast light on this heterogeneity. While there are fewer Republican than Democratic donors in our 2002-2010 dataset, the Republican donors tend to donate more. Thus, it is possible that Republican candidates are more responsive to their donors because they have fewer of them to pay attention to and because their pocketbooks speak more loudly -- findings that are consistent with the theory that donor access is the mechanism driving the relationship between donor ideology and candidate ideology. 

\vspace{20pt}

# 2. Nonresident Donors and Surrogate Representation

```{r section 2 setup, include = FALSE}

# Merging amount_house with amount
contrib <- left_join(amount_house, dime_all, by = "bonicarid") %>%
  mutate(cand_state = gsub("\\d\\d", "", cand_dist),
         donor_state = gsub("\\d\\d", "", contributordistrict00s)) %>%
  filter(donor_state != "DC") %>%
  mutate(out_state = case_when(
           donor_state != cand_state ~ 1,
           donor_state == cand_state ~ 0,
           TRUE ~ NA_real_
         ))

# Load replication data for Kujala (2020)
pct <- candidate0210_weighted %>% 
  mutate(outmaj = ifelse(pct_out > .5, 1, 0)) %>%
  summarise(maj = mean(outmaj, na.rm = TRUE))
pct <- as.numeric(pct) * 100
pct <- format(round(pct, 4), nsmall = 2)
```

The heightened importance of out-of-district donors and nationalization of politics begs the question of who legislators represent. Are representatives beholden to donors cutting them checks from the wealthy suburbs of New York and California at the expense of their actual constituents? To answer this question, we begin with an operationalization of these out-of-district donors. Ideally, campaign contribution records would reflect accurate donor location, allowing researchers to make precise inferences about in-district and out-of-district representation. However, contribution records can sometimes mislocate donors and place them in adjacent districts. In order to circumvent these data issues, we have instead constructed a measure of out-of-state donors and in-state donors. Although this operationalization does not fully address issues of district placement, it reduces the potential for measurement error. Substantively, this operationalization also has normative value. When making legislative decisions, representatives may consider not just their own territorial constituents, but also those living in adjacent districts who may commute to work in, shop in, visit family in, or otherwise have personal business in that representative's district. Thus, a measure of in-state versus out-of-state donors allows us to better measure the difference between "local" donors and "outsider" donors. For a concrete example, a donor from Alabama 7th giving to a candidate in Alabama 6th would be considered a "local," while a donor from California would be categorized as an "outsider."

Although recent scholarship points to the increasing influence of out-of-district fundraising in federal elections (Gimpel, Lee, and Pearson-Merkowitz, 2008; Hopkins, 2018), we find that out-of-state donations still represent a minority of contributions in most congressional races. As **Figure 3** demonstrates, among primary winners, incumbent candidates fundraise from out-of-state donors slightly more than do challengers, but only about 6.2% of candidates accept a majority of their donations from out-of-state sources.

\vspace{14pt}

```{r descriptive stats pct_out, echo=FALSE, warning=FALSE, message=FALSE}
#subset into incumbents, challengers and open-seat candidates
incumbents <- candidate0210_weighted[candidate0210_weighted$type_p == 1,]
challengers <- candidate0210_weighted[candidate0210_weighted$type_p == 2,]
opens <- candidate0210_weighted[candidate0210_weighted$type_p == 3,]

dat <- data.frame(dens = c(incumbents$pct_out, challengers$pct_out, opens$pct_out),
                  Legend = c(rep("Incumbents", nrow(incumbents)), 
                            rep("Challengers", nrow(challengers)), 
                            rep("Open-seat Candidates", nrow(opens))))

ggplot(dat, aes(x = dens, fill = Legend)) + geom_density(alpha = 0.5) + 
  xlab("Proportion of Fundraising from Out-of-State Donations") + 
  ylab("Density") + 
  ggtitle("Figure 3: Proportion of Out-of-State Donations by Candidate Type") + 
  geom_vline(xintercept = median(candidate0210_weighted$pct_out), color = "black") +
  annotate(geom = "text", x = .35, y = 3.5, label = "Median = 13.5%") +
  theme_bw()
```

**Figure 4** details these proportions by party. Although Democratic candidates seem to hold a slightly greater proportion of out-of-state donations, the parties are nearly identical. In this paper, we detail differences in donor influences by party, but proportions of donations from out-of-state sources are relatively uniform across partisan lines.

\vspace{14pt}

```{r descriptive stats pct_out party, echo = FALSE, warning = FALSE, message = FALSE}
dem <- candidate0210_weighted[candidate0210_weighted$democrat == 1,]
rep <- candidate0210_weighted[candidate0210_weighted$democrat == 0,]

dat2 <- data.frame(dens = c(dem$pct_out, rep$pct_out),
                   Legend = c(rep("Democrat", nrow(dem)),
                              rep("Republican", nrow(rep))))

ggplot(dat2, aes(x = dens, fill = Legend)) + geom_density(alpha = .5) + 
  xlab("Proportion of Fundraising from Out-of-State Donations") + 
  ylab("Density") + 
  ggtitle("Figure 4: Proportion of Out-of-State Donations by Candidate Party") + 
  scale_fill_manual(values=c("dodgerblue", "red")) +
  geom_vline(xintercept = median(candidate0210_weighted$pct_out), color = "black") +
  annotate(geom = "text", x = .3, y = 3.5, label = "Median = 13.5%") +
  theme_bw()
```

```{r regression prep, echo=FALSE, warning=FALSE, message=FALSE}
### Dems
dem_inc <- incumbents %>% 
  filter(., win_p == 1 & democrat == 1) %>%
  rename(Incumbent = distge_cf7) %>% 
  plm(Incumbent ~ distge_cf7_don*pct_out # Weighted donor distance from district*Proportion individual fundraising
      + distge_id7_p, # Primary constituency distance from district
      data = ., 
      index = "year", model = "within")

### Reps
rep_inc <- incumbents %>% 
  filter(., win_p == 1 & democrat == 0) %>%
  rename(Incumbent = distge_cf7) %>% 
  plm(Incumbent ~ distge_cf7_don*pct_out # Weighted donor distance from district*Proportion individual fundraising
      + distge_id7_p, # Primary constituency distance from district
      data = ., 
      index = "year", model = "within")

#Define 10th and 90th percentile proportion of fundraising from individuals for Rs and Ds
dems <- incumbents[incumbents$democrat == 1,]
reps <- incumbents[incumbents$democrat == 0,]
dems_10 <- quantile(dems$pct_out, .1, na.rm = TRUE)
dems_90 <- quantile(dems$pct_out, .9, na.rm = TRUE)
reps_10 <- quantile(reps$pct_out, .1, na.rm = TRUE)
reps_90 <- quantile(reps$pct_out, .9, na.rm = TRUE)
```

In order to measure whether out-of-state donations are influencing congressional candidates’ ideological positioning, we interact our variable for the proportion of out-of-state donations on weighted donor ideological distance from district in a multivariate regression.\footnote{For full regression table, see Appendix Section A.I.} We run 50,000 simulations using our coefficient estimates to predict divergence of incumbents from their districts. Our predictions isolate candidates at the 10th and 90th percentiles of their parties’ distribution of out-of-state fundraising. For Democrats, the 10th percentile is 6.5% of donations from out-of-state sources and the 90th percentile is 41.7%. For Republicans, the 10th percentile is 5% of donations from out-of-state sources and the 90th percentile is 32.3%. All other variables are held at their observed values. **Figure 5** represents the results for Republican incumbents and **Figure 6** represents the results for Democrat incumbents. In both **Figure 5** and **Figure 6**, the purple lines represent incumbent candidates at the 10th percentile of their respective party’s distribution of the proportion of out-of-state donations, whereas the green lines represent incumbent candidates at the 90th percentile. Each line is surrounded by a 95% confidence interval. 

\vspace{14pt}

```{r figures 5 and 6, echo=FALSE, warning=FALSE, message=FALSE}
#Simulate coefficients from multivariate normal
betas_rep <- rmvnorm(50000, mean = coef(rep_inc), sigma=vcov(rep_inc))
betas_dem <- rmvnorm(50000, mean = coef(dem_inc), sigma=vcov(dem_inc))

#Create matrix of observed covariate values
x_rep_10 <- x_rep_90 <- model.matrix(rep_inc)
x_dem_10 <- x_dem_90 <- model.matrix(dem_inc)

#Check that simulates betas line up with data
if (!all(colnames(betas_rep) == colnames(x_rep_10))) {stop('MISALIGNED REP DATA')}
if (!all(colnames(betas_dem) == colnames(x_dem_10))) {stop('MISALIGNED DEM DATA')}

#Create range of observed weighted donor distances for Rs and Ds
rep_seq <- seq(min(reps$distge_cf7_don, na.rm = TRUE), max(reps$distge_cf7_don, na.rm = TRUE), length.out = 25)
dem_seq <- seq(min(dems$distge_cf7_don, na.rm = TRUE), max(dems$distge_cf7_don, na.rm = TRUE), length.out = 25)

#Looping over range for Republicans
pred_rep <- sapply(rep_seq, function(i) {
  
  #10th percentile pct_out
  x_rep_10[, "pct_out"] <- reps_10
  x_rep_10[, "distge_cf7_don"] <- i
  x_rep_10[, "distge_cf7_don:pct_out"] <- i*reps_10
  
  #90th percentile pct_out
  x_rep_90[, "pct_out"] <- reps_90
  x_rep_90[, "distge_cf7_don"] <- i
  x_rep_90[, "distge_cf7_don:pct_out"] <- i*reps_90  
  
  pr_10_i <- colMeans(x_rep_10%*%t(betas_rep))
  pr_90_i <- colMeans(x_rep_90%*%t(betas_rep))
  
  return(c(i, quantile(pr_10_i, c(0.025,0.975)), mean(pr_10_i), quantile(pr_90_i, c(0.025,0.975)), mean(pr_90_i)))
})

pred_rep <- data.frame(t(pred_rep))
names(pred_rep) <- c("weighted_donor_distance", "rep_10_cilow", 
                     "rep_10_cihigh", "rep_10_mean", "rep_90_cilow", "rep_90_cihigh", "rep_90_mean")

#Looping over range for Democrats
pred_dem <- sapply(dem_seq, function(i) {
  
  #10th percentile pct_out
  x_dem_10[, "pct_out"] <- dems_10
  x_dem_10[, "distge_cf7_don"] <- i
  x_dem_10[, "distge_cf7_don:pct_out"] <- i*dems_10
  
  #90th percentile pct_out
  x_dem_90[, "pct_out"] <- dems_90
  x_dem_90[, "distge_cf7_don"] <- i
  x_dem_90[, "distge_cf7_don:pct_out"] <- i*dems_90  
  
  pr_10_i <- colMeans(x_dem_10%*%t(betas_dem))
  pr_90_i <- colMeans(x_dem_90%*%t(betas_dem))
  
  return(c(i, quantile(pr_10_i, c(0.025,0.975)), mean(pr_10_i), quantile(pr_90_i, c(0.025,0.975)), mean(pr_90_i)))
})

pred_dem <- data.frame(t(pred_dem))
names(pred_dem) <- c("weighted_donor_distance", "dem_10_cilow", 
                     "dem_10_cihigh", "dem_10_mean", "dem_90_cilow", "dem_90_cihigh", "dem_90_mean")

#Plotting Republicans
ggplot() + geom_line(data = pred_rep, col = 'darkorchid4', aes(x = weighted_donor_distance, y = rep_10_mean)) + 
  geom_ribbon(data = pred_rep, col = 'darkorchid4', fill = 'darkorchid4', 
              aes(x = weighted_donor_distance, ymin = rep_10_cilow, ymax = rep_10_cihigh), alpha = .25) + 
  ggtitle("Figure 5: Out-of-State Donor Effect on Republican Incumbents") +
  xlab("Weighted Donor Constituency Distance from District") + 
  ylab("Predicted Nominee Ideological Distance from District") + 
  geom_line(data = pred_rep, col = 'green4', aes(x = weighted_donor_distance, y = rep_90_mean)) + 
  geom_ribbon(data = pred_rep, col = 'green4', fill = 'green4',
              aes(x = weighted_donor_distance, ymin = rep_90_cilow, ymax = rep_90_cihigh), alpha = .25) +
  annotate(geom = "text", x = 1.25, y = 2.25, label = "10th percentile", color = "darkorchid4") +
  annotate(geom = "text", x = 1.5, y = 1, label = "90th percentile", color = "green4") +
  theme_bw()

#Plotting Democrats
ggplot() + geom_line(data = pred_dem, col = 'darkorchid4', aes(x = weighted_donor_distance, y = dem_10_mean)) + 
  geom_ribbon(data = pred_dem, col = 'darkorchid4', fill = 'darkorchid4', 
              aes(x = weighted_donor_distance, ymin = dem_10_cilow, ymax = dem_10_cihigh), alpha = .25) + 
  ggtitle("Figure 6: Out-of-State Donor Effect on Democratic Incumbents") + 
  xlab("Weighted Donor Constituency Distance from District") + 
  ylab("Predicted Nominee Ideological Distance from District") + 
  geom_line(data = pred_dem, col = 'green4', aes(x = weighted_donor_distance, y = dem_90_mean)) + 
  geom_ribbon(data = pred_dem, col = 'green4', fill = 'green4',
              aes(x = weighted_donor_distance, ymin = dem_90_cilow, ymax = dem_90_cihigh), alpha = .25) + 
  annotate(geom = "text", x = 2.5, y = .5, label = "90th percentile", color = "green4") + 
  annotate(geom = "text", x = 1.75, y = 1.5, label = "10th percentile", color = "darkorchid4") +
  theme_bw()

```

The positive slopes of all four lines indicate that incumbent nominees are responsive to in-district donor constituency ideological distance from the district. This result is unsurprising and reinforces Kujala (2020)’s original findings that legislators diverge from districts in part due to the ideological divergence of their in-district donors. For Democratic incumbents, there are stark differences in donor representation for those with large shares of out-of-state donations and those with mainly local contributions. The relative flatness of the 90th percentile line suggests that legislators with more out-of-state donations are less likely to respond to the divergence of their in-district donor class. In other words, out-of-state donations serve as a moderating force on the influence of in-district donors for Democratic incumbent candidates. For Republican incumbents, however, the nearly identical sloped lines indicate that candidates with larger shares of out-of-state donations are representing their in-district donors similarly to those candidates with mainly local donations. Even though there is significant overlap between confidence intervals for both sets of partisans, the differential in slopes for Democratic incumbents and the lack thereof for Republican incumbents suggests that variation exists across party lines with regard to the significance of out-of-state donations. 

What implications do these findings have for how we understand congressional representation? Our results suggest that out-of-state donations continue to be a minority of contributions received by candidates and do not necessarily dominate congressional fundraising as suggested by extant scholarship. However, we do find that these out-of-state donations are significant in the context of Democratic incumbents, as they moderate the extent to which candidates are responsive to their in-district donor bases. Republican candidates, however, continue to represent these donors regardless of increased outsider influence. In contrast with the somewhat pessimistic conclusions of current literature, our findings highlight that out-of-district donations are not yet subsuming representation away from in-district constituencies, although there is a slight moderating effect in the influence of in-district donors.

One limitation of our analysis refers back to data issues highlighted at the beginning of this section. Our operationalization of in-state and out-of-state donors allows us to make inferences about who our legislators represent at the state level, but we acknowledge that addressing the mislocation of donors will be important for future work on representation and campaign finance.  

\vspace{20pt}

# 3. Heterogeneous Effects by Candidate Fundraising

## Political Action Committees

Election spending is not limited only to what contributions candidates can solicit from individual donors. Rather, political action committees (PACs), independent expenditures, joint fundraising committees, party organizations, and other outside groups contribute to candidates or spend directly on their behalf. We would expect that donor influence would vary based on a candidate's fundraising composition. A well-connected party insider who can muster party money or PAC donations, for instance, may feel less obligation to solicit donations from individuals whose policy preferences she may then feel responsive to.

The DIME data contain total receipts from PACs as well as data on individual donations, allowing us to construct a measure of the proportion of fundraising receipts that come from individual donations versus political action committees for each candidate-year observation. **Figure 7** shows the distribution of this measure, disaggregated by candidate type. Incumbents are much more likely to have a significant portion of their fundraising come from PACs than are open-seat candidates, who themselves are slightly more likely to have a significant proportion of PAC money than are challengers. This finding makes intuitive sense. Previous research has shown that corporate donors are more risk-averse than individual donors and thus are unlikely to give to challengers and risk the ire of a sitting incumbent. Furthermore, challengers and open-seat candidates are less likely to have networks that would bring them into contact with corporate donors, compared to active legislators whose committee work connects them on a routine basis with corporate and special interest representatives. Because challengers' and open-seat candidates' fundraising pools exhibit less variation than incumbents', we limit the following analysis to incumbents.

```{r descriptive stats pct_ind, echo=FALSE, warning=FALSE, message=FALSE}
#subset into incumbents, challengers and open-seat candidates
incumbents <- candidate0210_weighted[candidate0210_weighted$type_p == 1,]
challengers <- candidate0210_weighted[candidate0210_weighted$type_p == 2,]
opens <- candidate0210_weighted[candidate0210_weighted$type_p == 3,]

dat <- data.frame(dens = c(incumbents$pct_ind, challengers$pct_ind, opens$pct_ind),
                  Legend = c(rep("Incumbents", nrow(incumbents)), 
                            rep("Challengers", nrow(challengers)), 
                            rep("Open-seat Candidates", nrow(opens))))

ggplot(dat, aes(x = dens, fill = Legend)) + geom_density(alpha = 0.5) + 
  ggtitle("Figure 7: Composition of Candidate Fundraising") + 
  xlab("Proportion of Fundraising from Individuals vs. PACs") + 
  ylab("Density")
```

To examine whether candidate fundraising composition has a moderating effect on donor influence, we interact this variable on weighted donor ideological distance from district and repeat the analysis above.\footnote{For full regression table, see Appendix Section A.II.} Using the coefficients estimated from our multivariate regression, we run 50,000 simulations to generate predicted values for nominee ideological distance from district for incumbent candidates at the 10th and 90th percentiles of their respective party's distribution of proportion of fundraising that comes from individuals, holding all other variables at their observed values. For Republicans, the 10th percentile places candidates at approximately 34% of fundraising from individuals; the 90th percentile is approximately 73% of fundraising from individuals. For Democrats, those corresponding values are 30% fundraising from individuals at the 10th percentile and 70% at the 90th percentile. We would expect there to be a positive relationship between the proportion of a candidate's fundraising that comes from individuals and that candidate's responsiveness to donors. Graphically, this implies that candidates at the 90th percentile should have a steeper slope over the range of observed values for weighted donor constituency distance from district than do candidates at the 10th percentile.

```{r pct_ind predicted plots, echo=FALSE, warning=FALSE, message=FALSE}
### Dems
dem_inc <- incumbents %>% 
  filter(., win_p == 1 & democrat == 1) %>%
  rename(Incumbent = distge_cf7) %>% 
  plm(Incumbent ~ distge_cf7_don*pct_ind # Weighted donor distance from district*Proportion individual fundraising
      + distge_id7_p, # Primary constituency distance from district
      data = ., 
      index = "year", model = "within")

### Reps
rep_inc <- incumbents %>% 
  filter(., win_p == 1 & democrat == 0) %>%
  rename(Incumbent = distge_cf7) %>% 
  plm(Incumbent ~ distge_cf7_don*pct_ind # Weighted donor distance from district*Proportion individual fundraising
      + distge_id7_p, # Primary constituency distance from district
      data = ., 
      index = "year", model = "within")

#Define 10th and 90th percentile proportion of fundraising from individuals for Rs and Ds
dems <- incumbents[incumbents$democrat == 1,]
reps <- incumbents[incumbents$democrat == 0,]
dems_10 <- quantile(dems$pct_ind, .1, na.rm = TRUE)
dems_90 <- quantile(dems$pct_ind, .9, na.rm = TRUE)
reps_10 <- quantile(reps$pct_ind, .1, na.rm = TRUE)
reps_90 <- quantile(reps$pct_ind, .9, na.rm = TRUE)

#Simulate coefficients from multivariate normal
betas_rep <- rmvnorm(50000, mean = coef(rep_inc), sigma=vcov(rep_inc))
betas_dem <- rmvnorm(50000, mean = coef(dem_inc), sigma=vcov(dem_inc))

#Create matrix of observed covariate values
x_rep_10 <- x_rep_90 <- model.matrix(rep_inc)
x_dem_10 <- x_dem_90 <- model.matrix(dem_inc)

#Check that simulates betas line up with data
if (!all(colnames(betas_rep) == colnames(x_rep_10))) {stop('MISALIGNED REP DATA')}
if (!all(colnames(betas_dem) == colnames(x_dem_10))) {stop('MISALIGNED DEM DATA')}

#Create range of observed weighted donor distances for Rs and Ds
rep_seq <- seq(min(reps$distge_cf7_don, na.rm = TRUE), max(reps$distge_cf7_don, na.rm = TRUE), length.out = 25)
dem_seq <- seq(min(dems$distge_cf7_don, na.rm = TRUE), max(dems$distge_cf7_don, na.rm = TRUE), length.out = 25)

#Looping over range for Republicans
pred_rep <- sapply(rep_seq, function(i) {
  
  #10th percentile pct_ind
  x_rep_10[, "pct_ind"] <- reps_10
  x_rep_10[, "distge_cf7_don"] <- i
  x_rep_10[, "distge_cf7_don:pct_ind"] <- i*reps_10

  #90th percentile pct_ind
  x_rep_90[, "pct_ind"] <- reps_90
  x_rep_90[, "distge_cf7_don"] <- i
  x_rep_90[, "distge_cf7_don:pct_ind"] <- i*reps_90  
  
  pr_10_i <- colMeans(x_rep_10%*%t(betas_rep))
  pr_90_i <- colMeans(x_rep_90%*%t(betas_rep))
  
  return(c(i, quantile(pr_10_i, c(0.025,0.975)), mean(pr_10_i), quantile(pr_90_i, c(0.025,0.975)), mean(pr_90_i)))
})

pred_rep <- data.frame(t(pred_rep))
names(pred_rep) <- c("weighted_donor_distance", "rep_10_cilow", 
                     "rep_10_cihigh", "rep_10_mean", "rep_90_cilow", "rep_90_cihigh", "rep_90_mean")

#Looping over range for Democrats
pred_dem <- sapply(dem_seq, function(i) {
  
  #10th percentile pct_ind
  x_dem_10[, "pct_ind"] <- dems_10
  x_dem_10[, "distge_cf7_don"] <- i
  x_dem_10[, "distge_cf7_don:pct_ind"] <- i*dems_10

  #90th percentile pct_ind
  x_dem_90[, "pct_ind"] <- dems_90
  x_dem_90[, "distge_cf7_don"] <- i
  x_dem_90[, "distge_cf7_don:pct_ind"] <- i*dems_90  
  
  pr_10_i <- colMeans(x_dem_10%*%t(betas_dem))
  pr_90_i <- colMeans(x_dem_90%*%t(betas_dem))
  
  return(c(i, quantile(pr_10_i, c(0.025,0.975)), mean(pr_10_i), quantile(pr_90_i, c(0.025,0.975)), mean(pr_90_i)))
})

pred_dem <- data.frame(t(pred_dem))
names(pred_dem) <- c("weighted_donor_distance", "dem_10_cilow", 
                     "dem_10_cihigh", "dem_10_mean", "dem_90_cilow", "dem_90_cihigh", "dem_90_mean")

#Plotting Democrats
ggplot() + geom_line(data = pred_dem, col = 'darkorchid4', aes(x = weighted_donor_distance, y = dem_10_mean)) + 
  geom_ribbon(data = pred_dem, col = 'darkorchid4', fill = 'darkorchid4', 
              aes(x = weighted_donor_distance, ymin = dem_10_cilow, ymax = dem_10_cihigh), alpha = .25) + 
  ggtitle("Figure 8: Fundraising Composition Effect on Democratic Incumbents") + 
  xlab("Weighted Donor Constituency Distance from District") + 
  ylab("Predicted Nominee Ideological Distance from District") + 
  geom_line(data = pred_dem, col = 'green4', aes(x = weighted_donor_distance, y = dem_90_mean)) + 
  geom_ribbon(data = pred_dem, col = 'green4', fill = 'green4',
              aes(x = weighted_donor_distance, ymin = dem_90_cilow, ymax = dem_90_cihigh), alpha = .25) +
  annotate("text", x=2, y=-.15, label="10th percentile", col="darkorchid4") + 
  annotate("text", x=1.25, y=,1.27,label="90th percentile",col="green4") + 
  geom_segment(aes(x=.55, y=-.45, xend=2.9, yend=1.9), linetype='dashed')
```

**Figure 8** presents the results for Democratic incumbents, and **Figure 9** presents the results for Republican incumbents. In both plots, the purple lines are the predicted nominee ideological distance from district and 95% confidence intervals for incumbent candidates at the 10th percentile of their respective party's individual fundraising proportion distribution, and the green lines are the corresponding estimates for incumbents at the 90th percentile. For Democrats, the relationship between weighted donor constituency distance from district, proportion of fundraising from individuals, and predicted nominee ideological distance from district is as we expected. Democratic incumbents in the 10th percentile of fundraising from individuals do not significantly change their ideological distance from their district as their donors' ideological distance from district changes, holding the values for primary constituency distance from district at their observed levels. Democratic incumbents at the 90th percentile of fundraising from individuals, on the other hand, appear significantly more responsive to their donors. While the two sets of predicted values are statistically indistinguishable for the large majority of the observed range of weighted donor constituency distance from district, we take this as suggestive evidence that incumbents' fundraising composition moderates donor influence. Note that if the relationship between candidate and donor ideological distance were one-to-one, we would see slopes parallel to the 45-degree dashed line. That we do not suggests there are limitations to the amount of influence donors exert over incumbent candidates -- which comes as no surprise, given that even Democratic incumbents at the 90th percentile generate more than a quarter of their campaign donations from PACs.

```{r pct_ind predicted plots figure 9, echo=FALSE, warning=FALSE, message=FALSE}
#Plotting Republicans
ggplot() + geom_line(data = pred_rep, col = 'darkorchid4', aes(x = weighted_donor_distance, y = rep_10_mean)) + 
  geom_ribbon(data = pred_rep, col = 'darkorchid4', fill = 'darkorchid4', 
              aes(x = weighted_donor_distance, ymin = rep_10_cilow, ymax = rep_10_cihigh), alpha = .25) + 
  ggtitle("Figure 9: Fundraising Composition Effect on Republican Incumbents") +
  xlab("Weighted Donor Constituency Distance from District") + 
  ylab("Predicted Nominee Ideological Distance from District") + 
  geom_line(data = pred_rep, col = 'green4', aes(x = weighted_donor_distance, y = rep_90_mean)) + 
  geom_ribbon(data = pred_rep, col = 'green4', fill = 'green4',
              aes(x = weighted_donor_distance, ymin = rep_90_cilow, ymax = rep_90_cihigh), alpha = .25) +
  annotate("text", x=1.25,y=1.1,label="10th percentile", col="darkorchid4") + 
  annotate("text",x=.75,y=2.1,label="90th percentile",col="green4") + 
  geom_segment(aes(x=.55,y=1.1,xend=1.8,yend=2.35), linetype='dashed')
```

For Republicans, however, there is little discernible difference between incumbents at the 10th and 90th percentile of fundraising composition. Both sets of Republican incumbents appear responsive to their donor constituency, with the former actually exhibiting a steeper relationship. Both sets of predictions are close to mirroring the 45-degree dashed line. The two sets of predicted values are statistically indistinguishable throughout the entire range of weighted donor ideological distance from district.

What explains the partisan difference in incumbent responsiveness to donor ideological distance from district? While we cannot make a causal claim, these results are consistent with donor access being the mechanism at play. Republican incumbents in our observational dataset collect fewer individual donations, but those contributions are on average larger than the typical Democratic contribution. This suggests that Republicans have fewer and more important donors to pay attention to and thus, perhaps, that Republican donors have an easier time gaining access to candidates. Future work should examine this further in a setting more conducive to making causal claims.

## Self-Funding Candidates

Another potential source of heterogeneity stemming from candidate fundraising is the presence in our sample of self-funders -- candidates who either do not accept or accept only nominal donations from individuals and PACs and rely instead on their personal wealth to fund their campaign. Our data contain information on the amount that candidates contribute to their own campaign, but because of the small sample size of this group, we are not able to confidently examine self-funders. We detail some preliminary findings in Appendix Section A.III.

\vspace{20pt}

# 4. Conclusion

Taken together, our findings paint a sobering picture of how money in politics shapes polarization and representation in the United States. As shown in Section 1, the ideology of House nominees is highly responsive to the ideology of both large and small donors -- a troubling finding given that donors of all sizes tend to be more ideologically extreme than even partisan primary constituencies. While both Democrats and Republicans are responsive to their donors' ideologies, Republican nominees appear more likely to correspond with their donors at the expense of their primary and general electorates. Sections 2 and 3 highlight facets of campaign fundraising that moderate the influence of in-district donor constituencies, but both of which have normatively questionable implications for representation. In Section 2, we find that as candidates draw more donations from out-of-state donors, they in turn become less responsive to the ideologically extreme donor constituencies in their districts. While this may be welcome news to proponents of decreasing ideological polarization in Congress, it suggests our traditional theories of representation are straining under an increasingly nationalized electoral context. What is more, as Section 3 indicates, there is suggestive evidence that as incumbent (Democratic) candidates draw more money from political action committees as opposed to individuals, they become less responsive to the constituency of individual donors in their district. This again may be welcomed by proponents of congressional moderation, but it has normatively questionable implications for theories of representation. 

There are inherent limitations with analyzing donor ideology on a single ideological dimension. Future research should look at district donors’ issue-specific preferences and donors’ relative influence on specific roll calls relative to primary and general electorate issue preferences. Extant research suggests donors are more likely to influence candidates on issues in which their preferences are distinct from partisans as a whole. Broockman and Malhotra (2020), for instance, find that Republican donors are more conservative than Republican non-donors on economic issues but similar on social issues, whereas Democratic donors are more liberal than Democratic non-donors on social issues but similar on economic issues. If this is true, we should expect differences in donor influence relative to primary electorate influence across issue areas.

Future research should also extend this analysis past 2010. The 2010 U.S. Supreme Court decision in \textit{Citizens United} fundamentally transformed the congressional fundraising landscape by ruling that the First Amendment prohibits the federal government from placing limits on campaign spending by independent expenditures. This controversial ruling effectively opened the floodgates to so-called dark money groups in federal elections. It remains to be seen whether donor influence has declined in light of outside groups' increased role in elections. Notably, \textit{Citizens United} has taken effect concurrently with renewed focus on grassroots campaigning tactics following Barack Obama's presidential victories in 2008 and 2012 and the growth of online platforms such as Democrats' ActBlue and Republicans' WinRed that facilitate candidates' solicitation of small-dollar donations. We leave it to future research to determine whether these developments have effectively counterbalanced the increased influence of outside groups or whether donor -- and particularly small-dollar donor -- influence has declined in the post-\textit{Citizens United} world.

\newpage

# References

\begingroup
\setlength{\parindent}{-0.2in}
\setlength{\leftskip}{0.2in}
\setlength{\parskip}{8pt}
\indent
Achen, C. H, & Bartels, L. M. (2016). \textit{Democracy for Realists: Why Elections Do Not Produce Responsive Government.} Princeton: Princeton University Press

Ansolabehere, S., Snyder, J. M., & Stewart, C. (2001). “Candidate Positioning in U.S. House Elections.” \textit{American Journal of Political Science, 45}(1), 136-159. https://doi.org/10.2307/2669364 

Ansolabehere, S., & Kuriwaki, S. (in press). “Congressional Representation: Accountability from the Constituent’s Perspective.” \textit{The Journal of Politics.}

Bawn, K., Cohen, M., Karol, D., Masket, S., Noel, H., & Zaller, J. (2012). “A Theory of Political Parties: Groups, Policy Demands and Nominations in American Politics.” \textit{Perspectives on Politics, 10}(3), 571-597. doi:10.1017/S1537592712001624

Bonica, A. (2016). Database on Ideology, Money in Politics, and Elections: Public version 2.0 [Computer file]. Stanford, CA: Stanford University Libraries. https://data.stanford.edu/dime 

Bonica, A., & Cox, G. W. (2018). “Ideological Extremists in the U.S. Congress: Out of Step but Still in Office.” \textit{Quarterly Journal of Political Science, 13}, 207-236. http://dx.doi.org/10.1561/100.00016073 

Broockman, D., & Malhotra, N. (2020). “What Do Partisan Donors Want?” \textit{Public Opinion Quarterly, 84}(1), 104-118. https://doi.org/10.1093/poq/nfaa001 

Canes-Wrone, B., Brady, D. W., & Cogan, J. F. (2002). “Out of Step, Out of Office: Electoral Accountability and House Members’ Voting.” \textit{American Political Science Review, 96}(1), 127-140. https://www.jstor.org/stable/3117814

Downs, A. (1957). \textit{An Economic Theory of Democracy.} New York: Harper.

Gimpel, J. G., Lee, F. E., & Kaminski, J. (2006). “The Political Geography of Campaign Contributions in American Politics.” \textit{The Journal of Politics, 68}(3), 626-639. https://www.jstor.org/stable/10.1111/j.1468-2508.2006.00450.x 

Gimpel, J. G., Lee, F. E., & Pearson-Merkowitz, S. (2008). “The Check Is in the Mail: Interdistrict Funding Flows in Congressional Elections.” \textit{American Journal of Political Science, 52}(2), 373-394. https://www.jstor.org/stable/25193819 

Hopkins, D. (2018). \textit{The Increasingly United States: How and Why American Political Behavior Nationalized.} Chicago: University of Chicago Press. 

Kujala, J. (2020). “Donors, Primary Elections, and Polarization in the United States.” \textit{American Journal of Political Science, 64}(3), 587-602. https://doi.org/10.1111/ajps.12477

Mansbridge, J. (2003). “Rethinking Representation.” \textit{American Political Science Review, 97}(4), 515-528. https://doi.org/10.1017/S0003055403000856 

Miller, W. E., & Stokes, D. E. (1963). “Constituency Influence in Congress.” \textit{American Political Science Review, 57}(1), 45-56. https://www.jstor.org/stable/1952717

Page, B. I., Seawright, J., & Lacombe, M. J. (2019). *Billionaires and Stealth Politics*. Chicago: The University of Chicago Press.

Pildes, R. H. (2019). “Small-Donor-Based Campaign-Finance Reform and Political Polarization.” *The Yale Law Journal Forum*, 149-170.

\endgroup

\newpage

# Appendix

### A.I: Regression Table for Interaction Between Donor Distance from District and Out-of-State Fundraising Proportion of Donations

```{r, results = 'asis', echo = FALSE}
### Dems
dem_inc <- incumbents %>% 
  filter(., win_p == 1 & democrat == 1) %>%
  rename(Incumbent = distge_cf7) %>% 
  plm(Incumbent ~ distge_cf7_don*pct_out # Weighted donor distance from district*Proportion out-of-state 
      + distge_id7_p, # Primary constituency distance from district
      data = ., 
      index = "year", model = "within")

### Reps
rep_inc <- incumbents %>% 
  filter(., win_p == 1 & democrat == 0) %>%
  rename(Incumbent = distge_cf7) %>% 
  plm(Incumbent ~ distge_cf7_don*pct_out # Weighted donor distance from district*Proportion out-of-state 
      + distge_id7_p, # Primary constituency distance from district
      data = ., 
      index = "year", model = "within")

### Dems
dem_chal <- challengers %>% 
  filter(., win_p == 1 & democrat == 1) %>%
  rename(Challenger = distge_cf7) %>% 
  plm(Challenger ~ distge_cf7_don*pct_out # Weighted donor distance from district*Proportion out-of-state 
      + distge_id7_p, # Primary constituency distance from district
      data = ., 
      index = "year", model = "within")

### Reps
rep_chal <- challengers %>% 
  filter(., win_p == 1 & democrat == 0) %>%
  rename(Challenger = distge_cf7) %>% 
  plm(Challenger ~ distge_cf7_don*pct_out # Weighted donor distance from district*Proportion out-of-state 
      + distge_id7_p, # Primary constituency distance from district
      data = ., 
      index = "year", model = "within")

### Dems
dem_open <- opens %>% 
  filter(., win_p == 1 & democrat == 1) %>%
  rename(OpenSeat = distge_cf7) %>% 
  plm(OpenSeat ~ distge_cf7_don*pct_out # Weighted donor distance from district*Proportion out-of-state 
      + distge_id7_p, # Primary constituency distance from district
      data = ., 
      index = "year", model = "within")

### Reps
rep_open <- opens %>% 
  filter(., win_p == 1 & democrat == 0) %>%
  rename(OpenSeat = distge_cf7) %>% 
  plm(OpenSeat ~ distge_cf7_don*pct_out # Weighted donor distance from district*Proportion out-of-state 
      + distge_id7_p, # Primary constituency distance from district
      data = ., 
      index = "year", model = "within")

stargazer(dem_inc, rep_inc, 
          dem_chal, rep_chal, 
          dem_open, rep_open,
          title = "The Effect of Partisan Constituencies on the Divergence of Nominees from Their District, 2002–10",
          dep.var.labels = c("Incumbents", "Challengers", "Open-Seat"),
          dep.var.caption = "",
          column.labels = rep(c("Dem", "Rep"), 3),
          covariate.labels = c("Weighted Donor \\\\ Constituency \\\\ Distance \\\\ from District",
                               "Proportion of \\\\ Out-of-State \\\\ Fundraising",
                               "Primary \\\\ Constituency \\\\ Distance \\\\ from District",
                               "Weighted Donor \\\\ x \\\\ Out-of-State"),
          column.sep.width = "-8pt",
          omit.stat = c("f", "adj.rsq"),
          digits = 2,
          header = FALSE)
```

\newpage

### A.II: Regression Table for Interaction Between Donor Distance from District and Fundraising Proportion from Individuals

```{r table A.X, echo=FALSE, results = 'asis'}
stargazer(dem_inc, rep_inc,
          title = "The Effect of Partisan Constituencies on the Divergence of Nominees from Their District, 2002–10",
          dep.var.labels = c("Incumbents"),
          dep.var.caption = "",
          column.labels = c("Democrats", "Republicans"),
          covariate.labels = c("Weighted Donor \\\\ Constituency \\\\ Distance \\\\ from District",
                               "Proportion of \\\\ Individual \\\\ Fundraising",
                               "Primary \\\\ Constituency \\\\ Distance \\\\ from District",
                               "Weighted Donor \\\\ x \\\\ Fundraising"),
          column.sep.width = "-11pt",
          omit.stat = c("f", "adj.rsq"),
          digits = 2,
          header = FALSE)
```

\newpage

### A.III: Self-Funding Candidates

Our data contain information on the amount candidates contributed to their campaigns from their own personal finances, allowing us to test whether there are heterogeneous effects of donor influence based on the proportion of a candidate's campaign coffers that are filled with one's own wealth. One would expect a negative relationship between the proportion of a candidate's fundraising that comes from their own finances and donor influence. Intuitively, the less a campaign is dependent on donor contributions for purchasing advertising time, paying staff and consultants and other activities, the less that candidate is likely to be responsive to her donors. However, due to small sample size of self-funding candidates, we are unable to confidently test this. In our dataset, only nine candidates (all challengers or open-seat candidates) self-funded the entirety of their campaign. No incumbent self-financed even a majority of their campaign. The median candidate was responsible for less than 1% of their own campaign receipts. Noting that the small number of observations demands extreme caution in interpreting any results, we proceed with caution. 

We repeat our regression of weighted donor constituency distance from district on nominee ideological distance from district among the subset of our candidate-year observations for which the majority of campaign receipts came from personal finances. We view this as a placebo test of sorts: If donor access is the likely mechanism behind the effect of donor ideology on candidate ideology, we would expect the effect to be much weaker or disappear entirely with this subset, for whom donor dollars are less critical to campaign success. Ideally, we would like to have set the threshold for proportion of self-financing to be higher (say, 90%), but the small sample size of self-funders precludes this. **Table 5** presents the results of our regression. 

Among the full sample, the coefficients on weighted donor and primary constituency ideological distance from district are both positive, but only the latter approaches statistical significance. Considering only Democratic candidates, the interpretation is largely the same: The coefficient on primary constituency distance from district is positive and statistically significant at $p<0.1$, and the coefficient on weighted donor distance from district is statisticallly indistinguishable from zero. This is essentially the relationship we expected: Among the subset of partial self-funders, weighted donor distance from district is less predictive of nominee ideological positioning than is primary constituency distance from district. Moving to Republican candidates, however, the results are different. Here, both coefficients are positive and of roughly equal magnitude, but neither are statistically distinguishable from zero. The lack of statistical significance for a sample of this size ($n=55$) can hardly be taken as decisive, but the difference in the results between partisans -- when weighed alongside the results throughout this paper -- is suggestive that Republican candidates are uniquely responsive to their donors and appears consistent with an access mechanism. Future research should bring in data from more elections to more authoritatively examine this relationship. 

```{r self-funders, echo=FALSE, warning=FALSE, message=FALSE, results = 'asis'}

### All
all_self <- candidate0210_weighted %>% 
  filter(., win_p == 1 & pct_self > .5) %>%
  plm(distge_cf7 ~ distge_cf7_don # Weighted donor distance from district
      + distge_id7_p # Primary constituency distance from district
      + democrat, 
      data = ., 
      index = "year", model = "within")

### Dems
dems_self <- candidate0210_weighted %>% 
  filter(., win_p == 1 & democrat == 1 & pct_self > .5) %>%
  plm(distge_cf7 ~ distge_cf7_don # Weighted donor distance from district
      + distge_id7_p, # Primary constituency distance from district
      data = ., 
      index = "year", model = "within")

### Reps
reps_self <- candidate0210_weighted %>% 
  filter(., win_p == 1 & democrat == 0 & pct_self >= .5) %>%
  plm(distge_cf7 ~ distge_cf7_don # Weighted donor distance from district
      + distge_id7_p, # Primary constituency distance from district
      data = ., 
      index = "year", model = "within")

stargazer(all_self, dems_self, reps_self,
          title = "The Effect of Constituency Ideology on the Ideological Positioning of Self-Funders, 2002–10",
          dep.var.labels = "",
          dep.var.caption = "",
          column.labels = c("All", "Democrats", "Republicans"),
          covariate.labels = c("Weighted Donor \\\\ Constituency \\\\ Distance \\\\ from District",
                               "Primary \\\\ Constituency \\\\ Distance \\\\ from District",
                               "Democrat"),
          column.sep.width = "-5pt",
          omit.stat = c("f", "adj.rsq"),
          digits = 2,
          header = FALSE)
```
